TY - GEN
T1 - Speech quality assessment using EEG signals
AU - Bar, Omri
AU - Shallom, Ilan D.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/4
Y1 - 2017/1/4
N2 - The listener experience in modern audio communication systems acts as a key indicator for the entire system quality. Consequently, speech quality assessment attracts great interest from both industry and academia. Common methods for speech quality assessment are either subjective or based on subjective experiments and therefore limited in their ability to produce unbiased results. In this paper, we introduce a new EEG-based distortion measure (EBDM) for speech quality assessment. The EEG signals are represented by multi-channel autoregressive parameters. These parameters are then used, in coordination with Dynamic Time Warping algorithm to compare EEG responses of two signals with different speech quality. The method demonstrates promising preliminary indications for the possibility of using EEG signals as an objective basis for assessing speech quality degradation levels. We believe that further improvements will allow such EEG-based methods to be competitive with standard ITU-T recommended quality testing.
AB - The listener experience in modern audio communication systems acts as a key indicator for the entire system quality. Consequently, speech quality assessment attracts great interest from both industry and academia. Common methods for speech quality assessment are either subjective or based on subjective experiments and therefore limited in their ability to produce unbiased results. In this paper, we introduce a new EEG-based distortion measure (EBDM) for speech quality assessment. The EEG signals are represented by multi-channel autoregressive parameters. These parameters are then used, in coordination with Dynamic Time Warping algorithm to compare EEG responses of two signals with different speech quality. The method demonstrates promising preliminary indications for the possibility of using EEG signals as an objective basis for assessing speech quality degradation levels. We believe that further improvements will allow such EEG-based methods to be competitive with standard ITU-T recommended quality testing.
UR - http://www.scopus.com/inward/record.url?scp=85014142116&partnerID=8YFLogxK
U2 - 10.1109/ICSEE.2016.7806163
DO - 10.1109/ICSEE.2016.7806163
M3 - Conference contribution
AN - SCOPUS:85014142116
T3 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
BT - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PB - Institute of Electrical and Electronics Engineers
T2 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Y2 - 16 November 2016 through 18 November 2016
ER -